Misinformation spread in social networks has become a critical focus as users rely on these platforms as a primary source of news [1]}. Current studies in this area have focused on rumor and misinformation detection with a primary focus on the network's role in information diffusion models [2]}, [3]}, [4]}, [5]}.
Other studies compare the behavior of traditional and alternative media [6]}, classify media sources into sub-categories of misinformation [7]}, or attempt to detect rumor-spreading users [8]}. These and other studies have found that the size and shape of (mis)information cascades within a social network depends heavily on the initial reactions of the users. Yet, we still lack an understanding of how users (human and automated alike) react to news sources of varying credibility and how their various response types contribute to the spread of (mis)information. The present work aims to fill this gap by labelling bot and human users' reactions to (mis)information posted by various news sources to measure how bot and human user reactions to deceptive news sources differ from their responses to trusted news sources.
